
# Modules installation, if prompted
# ! pip install pandas 
# ! pip install sklearn
# ! pip install plotly
# ! pip install matplotlib
# ! pip install voila


# Import modules and give aliases to it
# Modules for data reading
import pandas as pd
import sklearn as sk
import numpy as np

# Modules for data graphing
import plotly.graph_objects as go
import matplotlib.pyplot as plt

# Modules for model building
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV

# Modules for model testing
import math
from sklearn.metrics import mean_squared_error
from sklearn.metrics import accuracy_score, precision_score, recall_score,f1_score, confusion_matrix, classification_report
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.metrics import mean_absolute_error


# Reading CSV Files, the data used in this project
# Data for Dependent Variable
tpk_bps = pd.read_csv('Datasets/train-TPK_Hotel_berbintang_2020.csv') # from the committee

# Data for Independent Variables
tpk_harian = pd.read_csv('Datasets/train-online_booking_2020.csv') # from the committee
penerbangan = pd.read_csv('Datasets/train-penerbangan_2020.csv') # number of flight passengers, from BPS website
wisatawan = pd.read_csv('Datasets/train-wisatawan_domestik_2020.csv') # from Disparda Bali


# Remove possible nan value for data with the original form of daily historical data
tpk_harian.dropna()


# This function is retrieved from https://gist.github.com/Xylambda/b8f38dce74dd3d54ff906eebfe560ac0 
# which uses Fast Fourier Transform to denoise the possible noisy data on covid_harian, covid_harian_aktif and tpk_harian

def fft_denoiser(x, n_components, to_real=True):
    """Fast fourier transform denoiser.
    
    Denoises data using the fast fourier transform.
    
    Parameters
    ----------
    x : numpy.array
        The data to denoise.
    n_components : int
        The value above which the coefficients will be kept.
    to_real : bool, optional, default: True
        Whether to remove the complex part (True) or not (False)
        
    Returns
    -------
    clean_data : numpy.array
        The denoised data.
        
    References
    ----------
    .. [1] Steve Brunton - Denoising Data with FFT[Python]
       https://www.youtube.com/watch?v=s2K1JfNR7Sc&ab_channel=SteveBrunton
    
    """
    n = len(x)
    
    # compute the fft
    fft = np.fft.fft(x, n)
    
    # compute power spectrum density
    # squared magnitud of each fft coefficient
    PSD = fft * np.conj(fft) / n
    
    # keep high frequencies
    _mask = PSD > n_components
    fft = _mask * fft
    
    # inverse fourier transform
    clean_data = np.fft.ifft(fft)
    
    if to_real:
        clean_data = clean_data.real
    
    return clean_data


# Show information on data types
tpk_harian.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 696 entries, 0 to 695
Data columns (total 6 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   province            696 non-null    object
 1   type                696 non-null    object
 2   tanggal             696 non-null    object
 3   klasifikasi         696 non-null    object
 4   all_available_room  696 non-null    int64 
 5   room_total          696 non-null    int64 
dtypes: int64(2), object(4)
memory usage: 32.8+ KB


# Change the datatype of variable 'tanggal' into datetime (YEAR-MONTH-DATE)
tpk_harian['tanggal'] = pd.to_datetime(tpk_harian['tanggal'], format="%m/%d/%Y")


# Show first five rows of the data frame
tpk_harian.head()


# Show information on data types
tpk_harian.dtypes

province                      object
type                          object
tanggal               datetime64[ns]
klasifikasi                   object
all_available_room             int64
room_total                     int64
dtype: object


# Calculate used/booked rooms on tpk_harian
# the total of rooms minus the available rooms on that day (not booked)
tpk_harian['used_room'] = tpk_harian.room_total-tpk_harian.all_available_room


# Calculate the value of tpk_online (the percentage
# of booked rooms on total rooms
tpk_harian['tpk_online'] = tpk_harian.used_room/tpk_harian.room_total*100


# Shows data tpk_harian of which variables tpk_online and used room have been added into
tpk_harian


# Calculate upper quantile of the data
max_thresold = tpk_harian['tpk_online'].quantile(0.90)
max_thresold

50.37341670604346


# Calculate lower quantile of the data
min_thresold = tpk_harian['tpk_online'].quantile(0.10)
min_thresold

13.100003163420439


# Remove outliers from upper and lower quantiles
tpk_harian = tpk_harian[(tpk_harian['tpk_online']<max_thresold) & (tpk_harian['tpk_online']>min_thresold)]


# Denoise the possible noisy data
tpk_harian['tpk_online_denoised_fft'] = fft_denoiser(tpk_harian['tpk_online'], 10, to_real=True)
tpk_harian['tpk_online_denoised_fft']

<ipython-input-16-e0d2ec7322ec>:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  tpk_harian['tpk_online_denoised_fft'] = fft_denoiser(tpk_harian['tpk_online'], 10, to_real=True)

3      48.018343
5      42.963230
7      37.568041
9      35.800638
11     34.379302
         ...    
691    16.033055
692    47.276324
693    23.166606
694    44.618422
695    15.458028
Name: tpk_online_denoised_fft, Length: 556, dtype: float64


# Prepare variable 'tanggal' as index for grouping the data based on month (the following code)
tpk_harian = tpk_harian.set_index('tanggal')


# Make a new data frame, named covid_cases_agg 
# which groups the data into months using the median on the data, and 'tanggal' as the index
tpk_harian_agg = tpk_harian.groupby(pd.Grouper(freq='M')).median()


# Reset the index on new data frame and show the datatypes
tpk_harian_agg = tpk_harian_agg.reset_index()
tpk_harian_agg


# Show shape (dimension) of array
tpk_harian_agg.shape

(12, 6)


# Add a new column named 'Id' on the data frame tpk_harian_agg,
# value ranges from 1 to 12
# The display the data
tpk_harian_agg['Id'] = np.arange(start=1, stop=13, step=1)
tpk_harian_agg


# Add a new column named 'Id' on the data frame penerbangan
# value ranges from 1 to 12
# The display the data
penerbangan['Id'] = np.arange(start=1, stop=13, step=1)
penerbangan


# Add a new column named 'Id' on the data frame wisatawan
# value ranges from 1 to 12
# The display the data
wisatawan['Id'] = np.arange(start=1, stop=13, step=1)
wisatawan


# Join all the data frames (used or not used in the final models)
# based on the column ID into a new data frame named tpk_join.
# And display the first five rows of the data.
tpk_join = pd.merge(tpk_bps,tpk_harian_agg,on='Id',how='left')
tpk_join = pd.merge(tpk_join,penerbangan,on='Id',how='left')
tpk_join = pd.merge(tpk_join,wisatawan,on='Id',how='left')
tpk_join.head()


# Delete (drop) unneccessary variables
tpk_join = tpk_join.drop(['tanggal', 'Aggregate_var', 'Provinsi'], axis=1)


# menamplilkan lima data teratas dari tpk_join
tpk_join.head()


# Save the resulted data frame tpk_join into csv file in my local
tpk_join.to_csv('Results/SVR/tpk_join_v999991.csv')


# Display scatter plot
fig = go.Figure()
fig.add_trace(go.Scatter(x=tpk_join['Id'], y=tpk_join['TPK'],
                    mode='lines+markers',
                    name='TPK BPS'))
fig.add_trace(go.Scatter(x=tpk_join['Id'], y=tpk_join['tpk_online_denoised_fft'],
                    mode='lines+markers',
                    name='TPK Online'))
fig.show()


# Display scatter plot
# the variable 'penerbangan' is divided by 10000 to make it easier in understanding the graph
fig = go.Figure()
fig.add_trace(go.Scatter(x=tpk_join['Id'], y=tpk_join['TPK'],
                    mode='lines+markers',
                    name='TPK BPS'))
fig.add_trace(go.Scatter(x=tpk_join['Id'], y=tpk_join['penerbangan']/10000,
                    mode='lines+markers',
                    name='Flight Passengers'))
fig.show()


# Display scatter plot
# the variable 'wisatawan' is divided by 10000 to make it easier in understanding the graph
fig = go.Figure()
fig.add_trace(go.Scatter(x=tpk_join['Id'], y=tpk_join['TPK'],
                    mode='lines+markers',
                    name='TPK BPS'))
fig.add_trace(go.Scatter(x=tpk_join['Id'], y=tpk_join['wisatawan']/10000,
                    mode='lines+markers',
                    name='Domestic Tourists'))
fig.show()


# Choose X variables and Y variable from data frame tpk_join
tpk_bps = tpk_join['TPK']
tpk_online = tpk_join['tpk_online_denoised_fft']
penerbangan = tpk_join['penerbangan']
wisatawan = tpk_join['wisatawan']


# Correlation check
np.corrcoef(tpk_online, tpk_bps)

array([[1.        , 0.33625855],
       [0.33625855, 1.        ]])


# Correlation check
np.corrcoef(penerbangan, tpk_bps)

array([[1.        , 0.98720155],
       [0.98720155, 1.        ]])


# Correlation check
np.corrcoef(wisatawan, tpk_bps)

array([[1.        , 0.94955001],
       [0.94955001, 1.        ]])


# Display scatter plots for variables with strong correlations (tpenerbangan, wisatawan, tpk_bps_arima)
plt.scatter(penerbangan, tpk_bps)

<matplotlib.collections.PathCollection at 0x179bb6def10>


# Convert dataframe into numpy arrays
datatpk = tpk_online.to_numpy().reshape(-1,1)
dataterbang = penerbangan.to_numpy().reshape(-1,1)
datawisata = wisatawan.to_numpy().reshape(-1,1)

# Combine the chosen variables as DataX
dataX = np.hstack([datatpk,datawisata,dataterbang])
dataX
# tpk_bps

array([[3.74338774e+01, 8.79702000e+05, 1.09416900e+06],
       [3.95708699e+01, 7.21105000e+05, 7.72595000e+05],
       [2.55843384e+01, 5.67452000e+05, 5.27776000e+05],
       [2.27531346e+01, 1.75120000e+05, 5.08740000e+04],
       [3.10621692e+01, 1.01948000e+05, 4.04700000e+03],
       [3.26358252e+01, 1.37395000e+05, 1.22730000e+04],
       [3.03083601e+01, 2.29112000e+05, 4.34920000e+04],
       [3.20977009e+01, 3.55732000e+05, 8.47210000e+04],
       [3.72228774e+01, 2.83349000e+05, 8.13210000e+04],
       [3.55185007e+01, 3.37304000e+05, 9.95620000e+04],
       [3.87707490e+01, 4.25097000e+05, 1.69895000e+05],
       [2.44761062e+01, 3.82841000e+05, 1.89485000e+05]])


# Read CSV file
tpk_harian_test = pd.read_csv('Datasets/test-online_booking_2021.csv')

# Remove nan value
tpk_harian_test.dropna()

# Change the datatype of variable 'tanggal' into datetime (YEAR-MONTH-DATE)
tpk_harian_test['tanggal'] = pd.to_datetime(tpk_harian_test['tanggal'], format="%m/%d/%Y")

# Calculate used_room based based on available room and total room
# And use it to calculate tpk_online (used room percentage)
tpk_harian_test['used_room'] = tpk_harian_test['room_total']-tpk_harian_test['all_available_room']
tpk_harian_test['tpk_online'] = tpk_harian_test['used_room'] / tpk_harian_test['room_total']*100


# Denoise the possible noisy data covid_harian
tpk_harian_test['tpk_online_denoised_fft'] = fft_denoiser(tpk_harian_test['tpk_online'], 10, to_real=True)
tpk_harian_test['tpk_online_denoised_fft']

0      40.222881
1      11.626967
2      39.564663
3      10.403010
4      40.100590
         ...    
351    13.441035
352    40.089164
353    14.315314
354    37.725997
355    13.463850
Name: tpk_online_denoised_fft, Length: 356, dtype: float64


# Prepare variable 'tanggal' as index for grouping the data based on month (the following code)
tpk_harian_test = tpk_harian_test.set_index('tanggal')

# Make a new data frame, named tpk_harian_test_agg 
# which groups the data into months using the median on the data, and 'tanggal' as the index
tpk_harian_test_agg = tpk_harian_test.groupby(by=tpk_harian_test.index.month).mean()


# Resets the index on new data frame and show the datatypes
tpk_harian_test_agg = tpk_harian_test_agg.reset_index()
tpk_harian_test_agg


# Add a new column named 'Id' on the data frame tpk_harian_agg,
# value ranges from 1 to 6
# The display the data
tpk_harian_test_agg['Id'] = np.arange(start=1, stop=7, step=1)
tpk_harian_test_agg


# Read the csv file
penerbangan_test = pd.read_csv('Datasets/test-penerbangan_2021.csv')


# Add a new column named 'Id' on the data frame penerbangan
# value ranges from 1 to 6
# The display the data
penerbangan_test['Id'] = np.arange(start=1, stop=7, step=1)
penerbangan_test


# Read the csv file
wisatawan_test = pd.read_csv('Datasets/test-wisatawan_domestik_2021.csv')


# Add a new column named 'Id' on the data frame wisatawan
# value ranges from 1 to 6
# The display the data
wisatawan_test['Id'] = np.arange(start=1, stop=7, step=1)
wisatawan_test


# Join all the data frames (used or not used in the final models)
# based on the column ID into a new data frame named tpk_join_test.
# And display the first five rows of the data.
tpk_join_test = pd.merge(tpk_harian_test_agg,penerbangan_test,on='Id',how='left')
tpk_join_test = pd.merge(tpk_join_test,wisatawan_test,on='Id',how='left')
tpk_join_test


tpk_join_test.to_csv('Results/SVR/tpk_join_test_v999991.csv')


# Convert dataframe into numpy arrays
datatpk_test = tpk_join_test['tpk_online'].to_numpy().reshape(-1,1)
dataterbang_test = tpk_join_test['penerbangan'].to_numpy().reshape(-1,1)
datawisata_test = tpk_join_test['wisatawan'].to_numpy().reshape(-1,1)

# Combine the chosen variables as DataX_test
dataX_test = np.hstack([datatpk_test,datawisata_test,dataterbang_test])
dataX_test

array([[2.36134668e+01, 2.82248000e+05, 1.19160000e+05],
       [2.64804538e+01, 2.40608000e+05, 7.11220000e+04],
       [2.30833299e+01, 3.05579000e+05, 1.17088000e+05],
       [2.47654844e+01, 3.30593000e+05, 1.42329000e+05],
       [2.51025722e+01, 3.63959000e+05, 1.21076000e+05],
       [2.63921091e+01, 4.98852000e+05, 2.26287000e+05]])


# Read csv file
data_true = pd.read_csv('Datasets/sample-submission_webbali.csv')
# Get the true Y
tpk_true = data_true['TPK']


# Import model library
from sklearn.svm import SVR

# Create model instance
svr = SVR()

# Set model hyper parameters
parameters= {
    'kernel' : ['linear'],
    # 'degree' :  [2], # only significant for poly and sigmoid
    'gamma' : ['auto'],
    'tol' : [1.5],
    'C' : [1],
    'epsilon' : [3],
    'shrinking' : [False],
    'cache_size' : [200],    
    'shrinking' : [False],
    'max_iter' : [100]
},

# Make model pipeline
model = make_pipeline(StandardScaler(),GridSearchCV(svr,parameters,scoring='neg_mean_gamma_deviance', cv=12, verbose=1))

# Train model
model.fit(X=dataX, y=tpk_bps)

# Predict y
tpk_prediksi = model.predict(dataX)

# Display prediction in a graph
plt.figure(figsize=(12, 3))
plt.plot(tpk_online, label='TPK Online')
plt.plot(tpk_bps, label='TPK BPS')
plt.plot(tpk_prediksi, label='Prediksi dari Model')

plt.legend()
plt.show()

Fitting 12 folds for each of 1 candidates, totalling 12 fits


# Make data frame based on prediction
hasil = pd.DataFrame()
hasil['Id'] = np.arange(1,13)
hasil['TPK'] = tpk_prediksi


# Save the result into csv file
hasil.to_csv('Results/SVR/hasilv999991-svr-training.csv', index=None)


# Visualize data in matplotlib
temp = pd.DataFrame()
temp['tpk_online'] = tpk_online
temp['tpk_bps'] = tpk_bps
temp['tpk_prediksi'] = tpk_prediksi

temp = temp.sort_values(by='tpk_online')

plt.figure(figsize=(12,3))
plt.plot(temp['tpk_online'], temp['tpk_bps'], 'bo')
plt.plot(temp['tpk_online'], temp['tpk_prediksi'])

[<matplotlib.lines.Line2D at 0x179bba08a00>]


# Calculate model accuracy        
model_svr = make_pipeline(StandardScaler(), SVR(kernel='linear', C=1, gamma='auto', tol=1.5, epsilon=3, shrinking=False,
                                               cache_size=200, max_iter=100))

score_svr = cross_val_score(model_svr, dataX, tpk_bps, cv=12, scoring='neg_mean_gamma_deviance', verbose=1)
np.mean(abs(score_svr))

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s finished

0.32353938262930093


## Predict y based on X data set
tpk_prediksi = model.predict(dataX_test)
tpk_prediksi
# true: 11.15	8.99	10.24	10.09	10.35	16.68

array([10.00895387,  8.13057591, 10.49184745, 11.27913083, 11.57598922,
       15.95167003])


# Calculate Root Mean Squared Error (RMSE, the scoring in Kaggle)
mse = mean_squared_error(tpk_true, tpk_prediksi)
rmse_svr = math.sqrt(mse)
rmse_svr

0.961905177379139


# Calculate Mean Absolute Percentage Error (MAPE)
mape_svr = mean_absolute_percentage_error(tpk_true, tpk_prediksi)
mape_svr

0.08374976356222269


# Calculate Mean Absolute Error (MAE)
mae_svr = mean_absolute_error(tpk_true,tpk_prediksi)
mae_svr

0.8992946156370799


# Display Predicted Y and True Y in a graph
plt.figure(figsize=(12, 3))
plt.plot(datatpk_test, label='TPK Online Test')
plt.plot(tpk_true, label='TPK BPS True')
plt.plot(tpk_prediksi, label='TPK BPS Prediksi')

plt.legend()
plt.show()


# Make data frame based on prediction
hasil = pd.DataFrame()
hasil['Id'] = np.arange(1,7)
hasil['TPK'] = tpk_prediksi


# Save the result into csv file
hasil.to_csv('Results/SVR/hasilv999991-svr.csv', index=None)


# Comparing RMSE with the original obtained by instructor during Workshop
math.sqrt(68.88720689917587)

8.299831739208685

	tanggal	all_available_room	room_total	used_room	tpk_online	tpk_online_denoised_fft
0	2020-01-31	459.0	766.0	279.0	37.106918	37.433877
1	2020-02-29	433.5	713.0	284.0	39.448381	39.570870
2	2020-03-31	629.5	758.0	186.0	24.497126	25.584338
3	2020-04-30	21190.0	33285.0	9520.0	22.779846	22.753135
4	2020-05-31	16659.0	32539.0	15707.0	30.792608	31.062169
5	2020-06-30	33407.0	50624.0	16181.0	32.457119	32.635825
6	2020-07-31	34482.0	51258.0	15414.0	30.432621	30.308360
7	2020-08-31	2274.0	3793.0	1519.0	32.988680	32.097701
8	2020-09-30	29066.0	49852.0	19863.0	37.913000	37.222877
9	2020-10-31	29403.5	49999.5	18812.0	35.942146	35.518501
10	2020-11-30	27918.0	48904.0	19868.0	39.083442	38.770749
11	2020-12-31	522.0	650.0	146.0	23.817292	24.476106

	tanggal	all_available_room	room_total	used_room	tpk_online	tpk_online_denoised_fft	Id
0	2020-01-31	459.0	766.0	279.0	37.106918	37.433877	1
1	2020-02-29	433.5	713.0	284.0	39.448381	39.570870	2
2	2020-03-31	629.5	758.0	186.0	24.497126	25.584338	3
3	2020-04-30	21190.0	33285.0	9520.0	22.779846	22.753135	4
4	2020-05-31	16659.0	32539.0	15707.0	30.792608	31.062169	5
5	2020-06-30	33407.0	50624.0	16181.0	32.457119	32.635825	6
6	2020-07-31	34482.0	51258.0	15414.0	30.432621	30.308360	7
7	2020-08-31	2274.0	3793.0	1519.0	32.988680	32.097701	8
8	2020-09-30	29066.0	49852.0	19863.0	37.913000	37.222877	9
9	2020-10-31	29403.5	49999.5	18812.0	35.942146	35.518501	10
10	2020-11-30	27918.0	48904.0	19868.0	39.083442	38.770749	11
11	2020-12-31	522.0	650.0	146.0	23.817292	24.476106	12

	tanggal_ter	penerbangan	Id
0	1-Jan-20	1094169	1
1	1-Feb-20	772595	2
2	1-Mar-20	527776	3
3	1-Apr-20	50874	4
4	1-May-20	4047	5
5	1-Jun-20	12273	6
6	1-Jul-20	43492	7
7	1-Aug-20	84721	8
8	1-Sep-20	81321	9
9	1-Oct-20	99562	10
10	1-Nov-20	169895	11
11	1-Dec-20	189485	12

	tanggal_wis	wisatawan	Id
0	1-Jan-20	879702	1
1	1-Feb-20	721105	2
2	1-Mar-20	567452	3
3	1-Apr-20	175120	4
4	1-May-20	101948	5
5	1-Jun-20	137395	6
6	1-Jul-20	229112	7
7	1-Aug-20	355732	8
8	1-Sep-20	283349	9
9	1-Oct-20	337304	10
10	1-Nov-20	425097	11
11	1-Dec-20	382841	12

	Id	Provinsi	Tahun	Bulan	Aggregate_var	TPK	tanggal	all_available_room	room_total	used_room	tpk_online	tpk_online_denoised_fft	tanggal_ter	penerbangan	tanggal_wis	wisatawan
0	1	Bali	2020	Januari	NaN	59.29	2020-01-31	459.0	766.0	279.0	37.106918	37.433877	1-Jan-20	1094169	1-Jan-20	879702
1	2	Bali	2020	Februari	NaN	45.98	2020-02-29	433.5	713.0	284.0	39.448381	39.570870	1-Feb-20	772595	1-Feb-20	721105
2	3	Bali	2020	Maret	NaN	25.41	2020-03-31	629.5	758.0	186.0	24.497126	25.584338	1-Mar-20	527776	1-Mar-20	567452
3	4	Bali	2020	April	NaN	3.22	2020-04-30	21190.0	33285.0	9520.0	22.779846	22.753135	1-Apr-20	50874	1-Apr-20	175120
4	5	Bali	2020	Mei	NaN	2.07	2020-05-31	16659.0	32539.0	15707.0	30.792608	31.062169	1-May-20	4047	1-May-20	101948

PACKAGE INSTALLATIONS¶

However, after numerous trials, I found out that only 3 independent variables; tpk_online, penerbangan and wisatawan give significant decrease on the best model's RMSE.¶

The complete source codes of data pre-processing for the other insignificant variables and other failing models can be found in Nofriani_model_trials_v999991.ipynb.¶

READING CSV FILES¶

DATA DENOISING¶

CALCULATE DAILY OCCUPANCY RATE (tpk_harian)¶

OUTLIER DETECTION AND REMOVAL ON tpk_harian¶

CALCULATE MONTHLY DATA ON tpk_harian¶

MERGE DATA FRAME¶

EXPORT DATA FRAME¶

EXPLORATORY DATA ANALYSIS (EDA)¶

PREPARATION FOR VARIABLE CHOICES¶

CORRELATION BETWEEN X AND Y¶

COMBINING DATA_X FOR MODEL TRAINING (model.fit)¶

PREPARE TEST DATA for TPK PREDICTION in January to June 2021¶

tpk_online TEST¶

penerbangan TEST¶

wisatawan TEST¶

MERGE DATA TEST¶

PREPARING TRUE Y VALUE (TRUE tpk_bps)¶

BUILDING MODEL WITH SCIKIT LEARN¶

SUPPORT VECTOR REGRESSION¶

	province	type	tanggal	klasifikasi	all_available_room	room_total
0	Bali	Hotel	1/1/2020	Rated	2155	37756
1	Bali	Hotel	1/1/2020	Not Rated	163	392
2	Bali	Hotel	1/2/2020	Rated	3878	38814
3	Bali	Hotel	1/2/2020	Not Rated	211	415
4	Bali	Hotel	1/3/2020	Rated	5412	39084
...	...	...	...	...	...	...
691	Bali	Hotel	12/29/2020	Not Rated	475	568
692	Bali	Hotel	12/30/2020	Rated	24240	45600
693	Bali	Hotel	12/30/2020	Not Rated	427	551
694	Bali	Hotel	12/31/2020	Rated	26194	46764
695	Bali	Hotel	12/31/2020	Not Rated	441	523

	province	type	tanggal	klasifikasi	all_available_room	room_total
0	Bali	Hotel	2020-01-01	Rated	2155	37756
1	Bali	Hotel	2020-01-01	Not Rated	163	392
2	Bali	Hotel	2020-01-02	Rated	3878	38814
3	Bali	Hotel	2020-01-02	Not Rated	211	415
4	Bali	Hotel	2020-01-03	Rated	5412	39084

	tanggal	all_available_room	room_total	used_room	tpk_online	tpk_online_denoised_fft
0	1	17361.016129	27941.919355	10580.903226	23.613467	23.584929
1	2	15538.517857	26191.535714	10653.017857	26.480454	26.423566
2	3	16422.633333	26036.833333	9614.200000	23.083330	23.261825
3	4	17021.983333	27078.533333	10056.550000	24.765484	24.696790
4	5	17185.810345	27388.155172	10202.344828	25.102572	25.034087
5	6	16729.866667	27310.683333	10580.816667	26.392109	26.431097

	tanggal_ter	penerbangan	Id
0	1-Jan-21	119160	1
1	1-Feb-21	71122	2
2	1-Mar-21	117088	3
3	1-Apr-21	142329	4
4	1-May-21	121076	5
5	1-Jun-21	226287	6

	tanggal_wis	wisatawan	Id
0	1-Jan-21	282248	1
1	1-Feb-21	240608	2
2	1-Mar-21	305579	3
3	1-Apr-21	330593	4
4	1-May-21	363959	5
5	1-Jun-21	498852	6